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Finding a domain-appropriate sense inventory for semantically tagging a corpus

Published online by Cambridge University Press:  01 December 1998

ALESSANDRO CUCCHIARELLI
Affiliation:
Università di Ancona, Istituto di Informatica, Via Brecce Bianche, I60131 Ancona, Italy, e-mail: alex@inform.unian.it
PAOLA VELARDI
Affiliation:
Università di Roma ‘La Sapienza’, Dipartimento di Scienze dell'Informazione, Via Salaria 113, I00198 Roma, Italy, e-mail: velardi@dsi.uniromal.it

Abstract

Semantically tagging a corpus is useful for many intermediate NLP tasks such as: acquisition of word argument structures in sublanguages; acquisition of syntactic disambiguation cues; terminology learning; etc. The general idea is that semantic tags allow the generalization of observed word patterns, and facilitate the discovery of recurrent sublanguage phenomena and selectional rules of various types. Yet, as opposed to POS tags in morphology, there is no consensus in the literature about the type and granularity of the semantic tags to be used. In this paper, we argue that an appropriate selection of semantic tags should be domain-dependent. We propose a method by which we select from WordNet an inventory of semantic tags that are ‘optimal’ for a given corpus, according to a scoring function defined as a linear combination of general and corpus-dependent performance factors. We believe that an optimal selection of a category inventory is a necessary premise for obtaining better results in all lexically learning algorithms that are based on, or concerned with, semantic categorization of words. Furthermore, an adequate inventory (one which intuitively ‘fits’ with the semantics of a domain, e.g. phenomenon for Natural Science, or part, piece for a technical handbook) may facilitate the manual annotation of large corpora.

Type
Research Article
Copyright
© 1998 Cambridge University Press

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Footnotes

The method presented in this paper has been developed within the context of the ECRAN project LE 2110, funded by the European Community. One of the main research objectives of ECRA is lexical tuning, semantic tagging and sense disambiguation being two important and preliminary objectives. This paper approached the problem of adapting (tuning) a sense inventory to a given domain. We thank Christian Pavoni who developed much of the software used in this experiment, as well as all our partners in the ECRAN project.
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